MeasureRegrMSE

mlr_measures_regr.mse

Calls <code><a rd-options="Metrics:mse" href="/link/Metrics%3A%3Amse()?package=mlr3&version=0.1.2&to=Metrics%3Amse" data-mini-rdoc="Metrics:mse::Metrics::mse()">Metrics::mse()</a></code>.

datasets

Efficient, object-oriented programming on the building blocks of
machine learning. Provides 'R6' objects for tasks, learners, resamplings,
and measures. The package is geared towards scalability and larger datasets
by supporting parallelization and out-of-memory data-backends like
databases. While 'mlr3' focuses on the core computational operations,
add-on packages provide additional functionality.

Michel Lang

mlr3

Machine Learning in R - Next Generation

Bernd Bischl

Jakob Richter

Patrick Schratz

Giuseppe Casalicchio

Stefan Coors

Quay Au

Martin Binder

MeasureRegrMSE function

<a rd-options="R6:R6Class" href="/link/R6%3A%3AR6Class?package=mlr3&version=0.1.2&to=R6%3AR6Class" data-mini-rdoc="R6:R6Class::R6::R6Class">R6::R6Class</a> inheriting from <a rd-options="" href="/link/MeasureClassif?package=mlr3&version=0.1.2" data-mini-rdoc="mlr3::MeasureClassif">MeasureClassif</a>.

Format

<pre>MeasureRegrMSE$new()
mlr_measures$get("regr.mse")
msr("regr.mse")
</pre>

Construction

Calls <code><a rd-options='Metrics:mse' href='Metrics::mse()'>Metrics::mse()</a></code>.

Mean Squared Error Regression Measure — MeasureRegrMSE

<a rd-options='R6:R6Class' href='R6::R6Class'>R6::R6Class</a> inheriting from <a rd-options='' href='MeasureClassif'>MeasureClassif</a>.

MeasureRegrMSE: Mean Squared Error Regression Measure

Description

Arguments

Format

Construction

See Also